Antithrombotic Therapy for Stroke Prevention in Patients With Ischemic Stroke With Aspirin Treatment Failure
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Many older patients presenting with acute ischemic stroke were already taking aspirin before admission. However, the management strategy for patients with aspirin treatment failure has not been fully established. METHODS: We used data from the American Heart Association Get With The Guidelines Stroke Registry to describe discharge antithrombotic treatment patterns among Medicare beneficiaries with ischemic stroke who were taking aspirin before their stroke and were discharged alive from 1734 hospitals in the United States between October 2012 and December 2017. RESULTS: Of 261 634 ischemic stroke survivors, 100 016 (38.2%) were taking aspirin monotherapy before stroke. Among them, 44.4% of patients remained on aspirin monotherapy at discharge (20.9% 81 mg, 18.2% 325 mg, 5.3% other or unknown dose). The next most common therapy choice was dual antiplatelet therapy (24.6%), followed by clopidogrel monotherapy (17.8%). The remaining 13.2% of patients were discharged on either aspirin/dipyridamole, warfarin, or nonvitamin K antagonist oral anticoagulants with or without antiplatelet, or no antithrombotic therapy at all. CONCLUSIONS: Nearly half of patients with ischemic stroke while on preventive therapy with aspirin are discharged on aspirin monotherapy without changing antithrombotic class, while the other half are discharged on clopidogrel monotherapy, dual antiplatelet therapy, or other less common agents. These findings emphasize the need for future research to identify best management strategies for this very common and complex clinical scenario.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".